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How AI and Computer Vision can Help You Achieve Lean Construction

LEAN thinking aims to remove waste from a system to provide the customer with the value they want with the least resources necessary. Productivity and efficiency are crucial and wanted by every industry in the world, by everybody in the world. For the construction industry, a change that improves one step in a process can mean millions of dollars saved or days or weeks of work time spared from unexpected delays.

If we apply LEAN thinking to construction it means we’re trying to find ways to complete projects efficiently, on time and on budget; maximizing the resources spent on site for productivity and minimizing the resources that go to waste. Unfortunately, this industry is significantly less efficient than other sectors. 60% of time at a typical construction site is spent on equipment transportation delays, travel within the job site, late starts and early quits, personnel breaks, receiving instructions, and other delays. This means only 40% of the time is actually productive. Here are the 8 Wastes of LEAN thinking applied to the construction site.

A crack was found in the steel beams within the Salesforce Transit Center resulting in a closure.

Defects:

When quality within a project doesn’t measure up, it typically results in either rework or starting from scratch again; wasting precious resources and racking up additional unnecessary costs. A recent example of this is San Francisco’s new Transbay Transit Center, where a cracked beam shut down the entire building.

Preventing defects and mistakes can save projects time, resources and money. Computer Vision image recognition and classification systems can assess video data from work sites in real-time, identify poor workmanship, deviation from standardized work plans or compare work done against BIM specifications.

Overproduction and Inventory:

Though construction may not see things in terms of “production”, managing the flow of material and labor is very important on a construction site. Materials that come onto the site need to be used as soon as possible, because if it isn’t being used in the project, it is taking up space on the site that could be used for additional resources.

Cameras tracking the arrival of materials on a site can use Computer Vision and compare them to a project schedule, signaling under-deliveries in real-time and preventing delays. Neural Network Algorithms help developers and general contractors assess efficiency, quality and safety while identifying potential risks and bottlenecks at all times. Proactively reducing search times vs. having to sort and store just-in-case materials.

Waiting, Motion andTransportation:

From idle equipment to people who are waiting for materials or equipment to become available, every minute on a construction site is valuable. AI can track this information and transform these analytics into valuable insights to optimize your processes. On the other hand, constantly moving around heavy equipment is a hassle. It takes up time, energy, and resources to move not only the large machinery, but also the people who have to go out of their way, along with everything else to make space for it as well.

Extra-Processing and Under-utilized Resources:

Computer Vision can identify under-utilized resources and provide insights for optimal coordination and utilization of equipment, labor and material for real estate developers, owners and investors. By using technology to monitor this data, manual processing is removed from the workflow and entry-error possibility is eliminated.

The first step to optimizing your processes and reducing waste is recognizing their existence, and effectively identifying them. By using AI and Computer Vision, the job site becomes much more transparent and easy to examine. Previously endless confusing datasets become meaningful analytic insights that can drive immediate action.